Automated assessment or rating of image features has many applications. In image retrieval systems, the ranking algorithm can incorporate feature quality as one of the ranking factors. For picture editing software, feature quality may improve the overall editing process and produce more appealing and polished photographs. Existing methods utilize machine learning and statistical modeling techniques on hand-crafted features or generic image descriptors.
While handcrafted features are often inspired from photography or psychology literature, they share some known limitations. In particular, aesthetics-sensitive attributes are manually designed and have limited scope. Some effective attributes may not be discovered through this process. Further, because of the vagueness of certain photographic or psychologic rules and the difficulty in implementing them computationally, these handcrafted features are often merely approximations of such rules. There is a lack of a principled approach to improve the effectiveness of such features.
Generic image features have been proposed to address the limitations of handcrafted features. They use well-designed but more generic image features including scale-invariant feature transform (SIFT) and Fisher Vector. However, because they are meant to be generic, they are unable to attain the upper performance limits in feature-related problems.